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Category: robotics/AI – Page 11

AI and brain control: New system identifies animal behavior and silences responsible neurons in real time

A male fruit fly in a laboratory chamber extends his wings and vibrates them to produce his species’ version of a love song. A female fly stays nearby listening. Suddenly, a green light flashes across the chamber for a fraction of a second. The male’s song cuts off mid-note and his wings fold. The female, not impressed by the interrupted serenade, walks away. The culprit? An AI system that watched the male begin his courtship dance and shut down his song-producing brain cells.

Developed by scientists at Nagoya University and their collaborators from Osaka University and Tohoku University, the AI can watch and recognize animal behaviors and control the specific brain circuits that drive them.

Published in Science Advances, the study presents an advanced AI system that can identify which animal performs a behavior in a group and selectively target only that animal’s brain cells during social interactions.

Why the economics of orbital AI are so brutal

He’s not alone. xAI’s head of compute has reportedly bet his counterpart at Anthropic that 1% of global compute will be in orbit by 2028. Google (which has a significant ownership stake in SpaceX) has announced a space AI effort called Project Suncatcher, which will launch prototype vehicles in 2027. Starcloud, a startup that has raised $34 million backed by Google and Andreessen Horowitz, filed its own plans for an 80,000 satellite constellation last week. Even Jeff Bezos has said this is the future.

But behind the hype, what will it actually take to get data centers into space?

In a first analysis, today’s terrestrial data centers remain cheaper than those in orbit. Andrew McCalip, a space engineer, has built a helpful calculator comparing the two models. His baseline results show that a 1 GW orbital data center might cost $42.4 billion — almost 3x its ground-bound equivalent, thanks to the up-front costs of building the satellites and launching them to orbit.

JUST RECORDED: Elon Musk Announces MAJOR Company Shakeup

Elon Musk Announces MAJOR Company Changes as XAI/SpaceX ## Elon Musk is announcing significant changes and advancements across his companies, primarily focused on developing and integrating artificial intelligence (AI) to drive innovation, productivity, and growth ## ## Questions to inspire discussion.

Product Development & Market Position.

🚀 Q: How fast did xAI achieve market leadership compared to competitors?

A: xAI reached number one in voice, image, video generation, and forecasting with the Grok 4.20 model in just 2.5 years, outpacing competitors who are 5–20 years old with larger teams and more resources.

📱 Q: What scale did xAI’s everything app reach in one year?

A: In one year, xAI went from nothing to 2M Teslas using Grok, deployed a Grok voice agent API, and built an everything app handling legal questions, slide decks, and puzzles.

Continue reading “JUST RECORDED: Elon Musk Announces MAJOR Company Shakeup” | >

SpaceX Starthink: Building Earth’s Planetary Neocortex with Orbital AI

In a bold fusion of SpaceX’s satellite expertise and Tesla’s AI prowess, the Starthink Synthetic Brain emerges as a revolutionary orbital data center.

Proposed in Digital Habitats February 2026 document, this next-gen satellite leverages the Starlink V3 platform to create a distributed synthetic intelligence wrapping the planet.

Following SpaceX’s FCC filing for up to one million orbital data centers and its acquisition of xAI, Starthink signals humanity’s leap toward a Kardashev II civilization.

As Elon Musk noted in February 2026, ]

“In 36 months, but probably closer to 30, the most economically compelling place to put AI will be space.”

## The Biological Analogy.

Starthink draws from neuroscience: * Neural Cluster: A single Tesla AI5 chip, processing AI inference at ~250W, like a neuron group. * Synthetic Brain: One Starthink satellite, a 2.5-tonne self-contained node with 500 neural clusters, solar power, storage, and comms. * Planetary Neocortex: One million interconnected Brains forming a global mesh intelligence, linked by laser and microwave “synapses.”

Continue reading “SpaceX Starthink: Building Earth’s Planetary Neocortex with Orbital AI” | >

Why the Future of Intelligence Is Already Here | Alex Wissner-Gross | TEDxBoston

The future of intelligence is rapidly evolving with AI advancements, poised to transform numerous aspects of life, work, and existence, with exponential growth and sweeping changes expected in the near future.

## Questions to inspire discussion.

Strategic Investment & Career Focus.

🎯 Q: Which companies should I prioritize for investment or career opportunities in the AI era?

A: Focus on companies with the strongest AI models and those advancing energy abundance, as these will have the largest marginal impact on enabling the innermost loop of robots building fabs, chips, and AI data centers to accelerate exponentially.

Understanding Market Dynamics.

Continue reading “Why the Future of Intelligence Is Already Here | Alex Wissner-Gross | TEDxBoston” | >

Future Humans: The Coming Diversity of Engineered Bodies and Synthetic Minds

For the first time in Earth’s history, one species can rewrite its own genome, rebuild its own brain, and design entirely new forms of intelligence. That combination makes Homo sapiens look less like evolution’s end point and more like a transitional form: an ancestral species whose descendants may be biological, mechanical, or something in between. The way future humans remember us may depend on how seriously our generation takes its role as the first conscious ancestor.

Imagine a descendant civilization, thousands or millions of years from now, trying to reconstruct its origins. Its members might not have bones or blood. They might be born in free-fall habitats orbiting other stars, or instantiated as software in computational substrates that current engineers can barely imagine. Their analysts would comb through archives from a small blue planet called Earth and conclude that the strange, warlike primates who built the first rockets and the first neural networks were not the culmination of evolution, but an ancestral phase.

That premise — the idea that present-day humans are an ancestral species for future humans and other intelligent beings — is beginning to migrate from science fiction into serious scientific and philosophical discussion. Advances in gene editing, synthetic biology, space medicine, brain–computer interfaces and artificial intelligence all point toward a future in which “intelligent beings” no longer form a single species, or even share a single kind of body. The more that picture comes into focus, the more it forces a rethinking of what “being human” means.

AI Discovers Geophysical Turbulence Model

One of the biggest challenges in climate science and weather forecasting is predicting the effects of turbulence at spatial scales smaller than the resolution of atmospheric and oceanic models. Simplified sets of equations known as closure models can predict the statistics of this “subgrid” turbulence, but existing closure models are prone to dynamic instabilities or fail to account for rare, high-energy events. Now Karan Jakhar at the University of Chicago and his colleagues have applied an artificial-intelligence (AI) tool to data generated by numerical simulations to uncover an improved closure model [1]. The finding, which the researchers subsequently verified with a mathematical derivation, offers insights into the multiscale dynamics of atmospheric and oceanic turbulence. It also illustrates that AI-generated prediction models need not be “black boxes,” but can be transparent and understandable.

The team trained their AI—a so-called equation-discovery tool—on “ground-truth” data that they generated by performing computationally costly, high-resolution numerical simulations of several 2D turbulent flows. The AI selected the smallest number of mathematical functions (from a library of 930 possibilities) that, in combination, could reproduce the statistical properties of the dataset. Previously, researchers have used this approach to reproduce only the spatial structure of small-scale turbulent flows. The tool used by Jakhar and collaborators filtered for functions that correctly represented not only the structure but also energy transfer between spatial scales.

They tested the performance of the resulting closure model by applying it to a computationally practical, low-resolution version of the dataset. The model accurately captured the detailed flow structures and energy transfers that appeared in the high-resolution ground-truth data. It also predicted statistically rare conditions corresponding to extreme-weather events, which have challenged previous models.

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